Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (1): 101-109.doi: 10.13229/j.cnki.jdxbgxb20200796

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Game behavior and model of lane-changing on the internet of vehicles environment

Da-yi QU(),Kai-xian HEI,Hai-bing GUO,Yan-feng JIA,Tao WANG   

  1. School of Mechanical and Automotive Engineering,QingDao University of Technology,Qingdao 266520,China
  • Received:2020-10-18 Online:2022-01-01 Published:2022-01-14

Abstract:

In the vehicle network environment, the traffic system will present the mixed coexistence of intelligent connected vehicles and traditional artificially driven vehicles for a long time. Focusing on the new mixed traffic flow in the intelligent connected traffic environment, the decision-making model of vehicle lane change behavior was established. The dynamic risk model during lane-changing is introduced to establish the interaction between autonomous vehicles and traditional vehicles in the mixed traffic flow. The lane-changing behavior of autonomous vehicles in mixed traffic flow is modelled based on game theory. Lane-changing behavior between autonomous vehicles is the nature of the non-cooperative game. Vehicles seek for a lane with better driving conditions by taking their own driving state as the game benefit. The simulation results show that the game lane-changing model has higher lane utilization and safety stability than the traditional gap threshold acceptance model.

Key words: traffic engineering, lane-changing behavior, game theory, SUMO software, dynamic risk model

CLC Number: 

  • U491.2

Fig.1

Vehicles discretionary lane-changing"

Table 1

Payoff matrix"

参与者2
t1t2
参与者1r1u1r1,t1u2r1,t1u1r1,t2u2r1,t2
r2u1r2,t1u2r2,t1u1r2,t2u2r2,t2

Table 2

Mix strategy payoff matrix"

参与者2
c1c2
参与者1s1u1s1,c1u2s1,c1u1s1,c2u2s1,c2p
s2u1s2,c1u2s2,c1u1s2,c2u2s2,c21-p
概 率q1-q

Fig.2

Schematic diagram of vehicle lane changing process"

Fig.3

Collision probability function"

Fig.4

Dynamic risk coefficient graph"

Fig.5

Flow chart of vehicle lane changing decision"

Table 3

Game payoff matrix of model"

目标车辆
c1()c2()
受试车辆s1()u1s1,c1u2s1,c1u1s1,c2u2s1,c2p
s2()u1s2,c1u2s2,c1u1s2,c2u2s2,c21-p
概 率q1-q

Fig.6

Game payoff graph"

Fig.7

Dynamic risk model"

Fig.8

Collision probability and dynamic risk coefficient of CAV"

Table 4

Simulation vehicle parameters"

仿真车辆颜色amax/m?s-2vmax/m?s-1l/nm换道模式
HV蓝色2.633.334800LC2013
CAV粉色2.633.334800

动态风险模型/

博弈换道模型

Fig.9

Traditional traffic flow simulation experiment in group A"

Fig.10

Hybrid traffic flow simulation experiment in group B"

Fig.11

Simulation experiment of autonomous vehicle flow in group C"

Fig.12

Comparison of average vehicle speed between three models"

Fig.13

Comparison of vehicle passing Numbers under three models"

Table 5

Comparison of accident number under three models"

车流密度/

(veh·h-1

A组事故数B组事故数C组事故数
2 000000
2 250000
2 500000
2 750000
3 000100
3 250110
3 500300
3 750310
4 000210

Fig.14

Average velocity of traffic flow under different CAV ratios"

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